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Functions can also be plotted by generating a line that approximates the function. In fact, this is what happens behind the scenes when the 'plot' function is called.
Patrick van der Pijl: On January 4th 2011 Alex Osterwalder reinversed the business model of Facebook. On his blog he reconstructed together with his smart and loyal followers the model of Facebook. We thought it would be great to build on the work that has been done by visualizing this business model. More on www.businessmodelsinc.com - Illustration: Joeri Lefevre
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
Visualisation of emails received in a subfolder using the Java based Processing toolkit.
The long lines separate years, each row is a separate email address, the length of the green is the size of the email.
Email data exported from Outlook into Access then filtered into text doc for reading by Processing. Contact me if you want the script.
If you look deep in to the image, you can visualize alot of funny stuff from this image...give it a try! :)
What you see from distance in the image is a sand bank with small trees which is eventually going to be an island.
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
Paper at www.stat.columbia.edu/~gelman/research/published/signif4.pdf (PDF)
Let's say that a country, Lagutrop, wants to increase its PISA results and, before deciding on policy, the decision-makers want to know what works by running experiments or studies of intervention effectiveness. These studies compare two variables with similar intervention scales, say expenditure in computer-assisted learning (U) and expenditure in teacher selection and incentive systems (V).
In Study 1, data is collected measuring the effect of U and V (possibly with a lot of covariates). The parameter estimates for the effect of U and V are given by the two bell-curve-like curves on the left above.
Conclusion (as is traditionally presented): Lagutrop should invest in teacher selection and incentive systems, since computer-aided education has no significant effect.
Gelman and Stern problem 1: but the difference between the effects is itself non-significant! If significance is the criterion for disposing of U, then it should also be explained to the decision-makers that significance cannot be used to separate U from V. Specifically, policy-makers in Lagutrop should be told that the rejection of computer-aided education is based on a criterion that also suggests that computer-based education is as effective as teacher recruitment and incentives.
Meanwhile, another group of researchers run a single-variable study (Study 2) considering only the effects of spending money on teachers (in Lagutrop this study would probably have been done by teachers :-).
The results of Study 2 are then presented as supporting the conclusions of Study one, phrased as "Expenditure on teachers shows a significant effect on PISA scores in both studies."
Gelman and Stern problem 2: Studies 1 and 2 predict very different effect sizes for variable V; why the discrepancy? How can two parameter estimates that are significantly different from each other be considered corroboration?
My own take on this problem 2 is the following: suppose the policy-makers in Lagutrop have to decide how much to allocate to this PISA-improvement project, out of a budget that includes other considerations (national defense, jobs for the families and friends of the politicians, police, fire-fighters, etc.). Budgeting will require forecasting. Which of the parameter estimates for effect size will they use to build a forecasting model? Since the two estimates are significantly different, any attempt at aggregation would violate the basic meaning of that significance.
That's what we engineers call a serious execution problem.
(Reblogged at my personal blog.)
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Part of a 5 house visualization project for ARCHIPOD.
Furniture models in this image are part of the model supplied in the latest 3dallusions - Charette#15 - Quinta House
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
I can't see
I can't walk alone
I lack some physical skills
But, I won't knee to blindess
I won't be completely depedent on the others
I won't stay without any work
I have the heart to visualize the feelings
I have the mind to comprehend the life
I have the soul to be a free human
[Abraaj. January 2007]
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Series of Kuwait's portraits (winter 06-2007)
I saw this blind old man at Al-Mubarakiah Market in Kuwait City
Canon EF 70-200mm f/2.8L
editing: RAW processing and Photoshop editing
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
Terms of Use:
Credit: Original Data from JWST: NASA, ESA, CSA, STScI
Data Processing: Jac Berne (astronomical data visualization)
What you see:
Core of the Galaxy NGC 1365 as a close up from WEBB.
Compare with HUBBLE:
www.flickr.com/photos/geckzilla/48601583091
Details:
This image is a free artistic interpretation of scientific data.
F200W/F770W/F1000W/F1130W/F2100W
Scientific background information:
Proposal GO 2107
A JWST-HST-VLT/MUSE-ALMA Treasury of Star Formation in Nearby Galaxies
Principal Investigator: Janice Lee
PI Institution: University of Arizona
www.stsci.edu/jwst/phase2-public/2107.pdf
Artist Jac Berne is a citizen scientist who practice artistic research in the field of astronomy as a hobby.
AVAO - Artistic Illustration Technologies for the Hyper Realistic Spatial Visualisation of Astronomical Objects
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
I was astounded by Bill Rankin's map of Chicago's racial and ethnic divides and wanted to see what other cities looked like mapped the same way. To match his map, Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Gray is Other, and each dot is 25 people. Data from Census 2000. Base map © OpenStreetMap, CC-BY-SA
Maps of racial and ethnic divisions in US cities, inspired by Bill Rankin's map of Chicago, updated for Census 2010.
Red is White, Blue is Black, Green is Asian, Orange is Hispanic, Yellow is Other, and each dot is 25 residents.
Data from Census 2010. Base map © OpenStreetMap, CC-BY-SA